Bayesian Inference, Minimum Description Length Principle, and Learning by Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{zhang:1995:bimdl,

• author = "Byoung-Tak Zhang and Heinz M{\"u}hlenbein",
• title = "Bayesian Inference, Minimum Description Length Principle, and Learning by Genetic Programming",
• booktitle = "Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications",
• year = "1995",
• editor = "Justinian P. Rosca",
• pages = "1--5",
• address = "Tahoe City, California, USA",
• month = "9 " # jul,
• keywords = "genetic algorithms, genetic programming",
• URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/zhang_1995_bimdl.pdf",
• size = "5 pages",
• abstract = "adaptive search technique which dynamically balances the ratio of training accuracy to complexity of programs to achieve parsimonious solutions without loosing population diversity.",
• notes = "part of \cite{rosca:1995:ml}",

}

Genetic Programming entries for Byoung-Tak Zhang Heinz Muhlenbein

Citations